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dc.contributor.authorGaudreau-Balderrama, Amanda
dc.date.accessioned2017-08-29T13:55:18Z
dc.date.issued2017
dc.identifier.urihttps://hdl.handle.net/2144/23684
dc.description.abstractThe primary aim of this thesis is to develop image processing algorithms to quantitatively determine the link between traumatic brain injury (TBI) severity and chronic traumatic encephalopathy (CTE) neuropathology, specifically looking into the role of blood-brain barrier disruption following TBI. In order to causally investigate the relationship between the tau protein neurodegenerative disease CTE and TBI, mouse models of blast neurotrauma (BNT) and impact neurotrauma (INT) are investigated. First, a high-speed video tracking algorithm is developed based on K-means clustering, active contours and Kalman filtering to comparatively study the head kinematics in blast and impact experiments. Then, to compare BNT and INT neuropathology, methods for quantitative analysis of macroscopic optical images and fluorescent images are described. The secondary aim of this thesis focuses on developing methods for a novel application of metallomic imaging mass spectrometry (MIMS) to biological tissue. Unlike traditional modalities used to assess neuropathology, that suffer from limited sensitivity and analytical capacity, MIMS uses a mass spectrometer -- an analytical instrument for measuring elements and isotopes with high dynamic range, sensitivity and specificity -- as the imaging sensor to generate spatial maps with spectral (vector-valued) data per pixel. Given the vector nature of MIMS data, a unique end-to-end processing pipeline is designed to support data acquisition, visualization and interpretation. A novel multi-modal and multi-channel image registration (MMMCIR) method using multi-variate mutual information as a similarity metric is developed in order to establish correspondence between two images of arbitrary modality. The MMMCIR method is then used to automatically segment MIMS images of the mouse brain and systematically evaluate the levels of relevant elements and isotopes after experimental closed-head impact injury on the impact side (ipsilateral) and opposing side (contralateral) of the brain. This method quantifiably confirms observed differences in gadolinium levels for a cohort of images. Finally, MIMS images of human lacrimal sac biopsy samples are used for preliminary clinicopathological assessments, supporting the utility of the unique insights MIMS provides by correlating areas of inflammation to areas of elevated toxic metals. The image processing methods developed in this work demonstrate the significant capabilities of MIMS and its role in enhancing our understanding of the underlying pathological mechanisms of TBI and other medical conditions.en_US
dc.language.isoen_USen_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0
dc.subjectEngineeringen_US
dc.subjectLA-ICP-MSen_US
dc.subjectLaser ablation inductively coupled plasma mass spectrometryen_US
dc.subjectPathological segmentationen_US
dc.titleMethods and algorithms for quantitative analysis of metallomic images to assess traumatic brain injuryen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2017-07-10T01:16:33Z
dc.description.embargo2019-07-09T00:00:00Z
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplineElectrical & Computer Engineeringen_US
etd.degree.grantorBoston Universityen_US


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Attribution-NonCommercial-ShareAlike 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International